CAU_KU team's submission to ADD 2022 Challenge task 1: Low-quality fake audio detection through frequency feature masking

02/09/2022

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by   Il-Youp Kwak, et al.

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This technical report describes Chung-Ang University and Korea University (CAU_KU) team's model participating in the Audio Deep Synthesis Detection (ADD) 2022 Challenge, track 1: Low-quality fake audio detection. For track 1, we propose a frequency feature masking (FFM) augmentation technique to deal with a low-quality audio environment. applied. We applied FFM and mixup augmentation on five spectrogram-based deep neural network architectures that performed well for spoofing detection using mel-spectrogram and constant Q transform (CQT) features. Our best submission achieved 23.8